Multivariable Simulation-Based Capacity Estimation for Decentralised Air Traffic Control. A Case Study on Future North Atlantic Operations

The transition from current centralised to future decentralised airspace concepts offers potential to increase airspace capacity. This is particularly relevant for intercontinental airspaces, such as the North Atlantic, with limited terrestrial infrastructure restricting the centralised coordination. Decentralised airspace concepts are defined by a partial transfer of the separation assurance responsibilities from a central unit to the flight deck. The capacity of centralised airspace concepts is mainly estimated based on the controller workload. Due to the transfer of the separation assurance responsibilities it is no longer possible to estimate the capacity and assess the benefits based on this parameter for decentralised airspace concepts. In order to evaluate the potential capacity benefits of decentralised concepts, new metrics are required. This paper addresses this problem in two stages. First, a simulation-based concept named Multivariable Simulation-based Capacity Estimation for Decentralised Air Traffic Control (MuSCEDA) to provide capacity estimates independent of the controller workload is described and second, an advanced Airborne Separation Assurance Systems (ASAS) algorithm to further increase airspace capacity is implemented. Both are evaluated for a simplified North Atlantic subspace in a fast-time simulation. The framework estimates the capacity by defining critical thresholds across three key categories: Safety (Intrusion Prevention Rate, Intrusion Severity), Complexity (Domino Effect Parameter) and Efficiency (Delay, Additional Fuel Consumption). Unlike conventional separation assurance algorithms, the advanced algorithm features an assigned control logic tailored to the characteristics of NAT traffic, where aircraft mostly operate in cruise-flight close to their maximum Mach number and maximum operating altitude. The simulation results demonstrate that with the framework and a conventional separation algorithm the capacity can be estimated. However, some capacity limitations remained with regard to the conventional algorithm. The advanced algorithm, especially the implemented control logic, improved the results by preventing more separation infringements and increased efficiency. With the proposed concept it is possible to make robust, simulation-based assessments regarding airspace capacity for decentralised airspace concepts. Based on these results, a full-scale simulation study should be carried out under realistic operating conditions.